This year the PSI Training Committee are delighted to offer two pre-conference courses, set to run on Sunday 11 June 2023. They will be held at the same location used for the conference itself – at the Novotel London West.

The courses will run on Sunday 11 June 13:00 - 17:00. The courses run simultaneously and so individuals must register for their preferred course. 

Pre-conference course 1: Increasing your agility

Whether you are working on a project, company or personal objective, the ways in which we work will drive our impact and efficiency. Agile is an effective way of working to help balance and deliver on your priorities. This engaging and interactive session on agile ways of working will help you prioritise and deliver more effectively. You will leave the session with a clear understanding of what agile is, a range of tools to add to your professional toolkit and a personal action plan for how you can apply them. The session is highly interactive and includes a fully immersive workshop where you will be collaborating with other participants. This gives you the opportunity to practice and refine your new skills. The workshop will be run by our partner JCURV.

Course pre-requisites:

• No prior knowledge of agile ways of working are required.

Course instructors: 

• Payal Jain (Managing Director, JCURV)
  
• Ben Beavers (Director, JCURV)

About JCURV :
JCURV is an award winning consultancy firm whose mission is to increase our clients’ agility, so they can thrive in an increasingly uncertain world. It helps clients with:

• Increasing enterprise-wide agility
• Accelerating innovation
• Programme and project acceleration
• Rapid definition and strategy mobilisation
• Extracting value from data at pace
  

Pre-conference course 2: Improving Precision and Power in Randomized Trials by Leveraging Baseline Variables

In May 2021, the U.S. Food and Drug Administration (FDA) released a revised draft guidance for the industry on “Adjustment for Covariates in Randomized Clinical Trials for Drugs and Biological Products”. Covariate adjustment is a statistical analysis method for improving precision and power in clinical trials by adjusting for pre-specified, prognostic baseline variables. Here, the term “covariates” refers to baseline variables, that is, variables that are measured before randomization such as age, gender, BMI, comorbidities. The resulting sample size reductions can lead to substantial cost savings, and also can lead to more ethical trials since they avoid exposing more participants than necessary to experimental treatments. Though covariate adjustment is recommended by the FDA and the European Medicines Agency (EMA), many trials do not exploit the available information in baseline variables or only make use of the baseline measurement of the outcome.

In Part 1, we introduce the concept of covariate adjustment. In particular, we explain what covariate adjustment is, how it works, when it may be useful to apply, and how to implement it (in a preplanned way that is robust to model misspecification) for a variety of scenarios.

In Part 2, we present a new statistical method that enables us to easily combine covariate adjustment with group sequential designs. The result will be faster, more efficient trials for many disease areas, without sacrificing validity or power. This approach can lead to faster trials even when the experimental treatment is ineffective; this may be more ethical in settings where it is desirable to stop as early as possible to avoid unnecessary exposure to side effects.

In Part 3, we demonstrate the impact of covariate adjustment using completed trial data sets in multiple disease areas. We provide step-by-step, clear documentation of how to apply the software in each setting. Participants will have the time to apply the software tools on the different datasets in small groups.

Course pre-requisites:

• Participants will need to bring a laptop with R installed
• A basic knowledge about R is desirable, but not necessary as there will be tutorials available where the focus is on the output and interpretation rather than on the code
• Participants should be familiar with concepts like Type I error, power, bias and variance

Course instructors: Kelly Van Lancker (Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium), Josh Betz (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A.) and Michael Rosenblum (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A.)

The number of places on these courses are limited, register today!

Course Fees

The registration fees for the pre-conference courses are as follows.

All amounts are in GBP and include VAT.

The courses are now available to register to attend alongside your event registration, here.